Spatial-division multiple-access (SDMA) is a technique used in some cellular telephone systems to increase the network throughput. In an SDMA system, an access point (AP) includes multiple antennas, which are used communicate with multiple mobile units simultaneously using different beam patterns. In comparison with other cellular systems that do not utilize SDMA, the throughput of an SDMA system can be multiplied by approximately the number of antennas at an AP, without the need for increased spectrum usage.
When an AP sends signals to a group of mobile units simultaneously, the AP pre-compensates for the interference (e.g., crosstalk) between mobile units so that each mobile unit only receives its intended signal without interference from the signals intended for other mobile units. Similarly, when the AP receives signals from a group of mobile units, the AP cancels the interference, and detects each of the individual units' data. This process of interference cancellation is referred to a zero-forcing beamforming.
In order to achieve zero-forcing beamforming, current SDMA systems require channel state information, which is acquired through closed-loop training procedures conducted between the mobile units and the AP. Because channel asymmetry exists between the uplink channel and the downlink channel, the training process involves two phases.
During one phase of training, each mobile unit, in turn, sends a signal to the AP. Based on the signals, the AP estimates an uplink channel attenuation matrix, which describes the gain characteristics between the input of the mobile units' transmit chains to the output of the AP's receive chains. During a second phase of training, the AP sends a signal to each mobile unit. Each mobile unit estimates the channel attenuation, and it returns this information to the AP. From this information, the AP estimates a downlink channel attenuation matrix, which describes the gain characteristics between the input of the AP's transmit chain to the output of the mobile units' receive chains. Because the channel is continuously and significantly changing, this training process is repeated frequently (e.g., at least every second up to every data packet).
The closed-loop training process is highly complex, and it consumes significant amounts of time, processing capacity, and bandwidth. In addition, the ever-changing channel conditions cause the attenuation matrices to almost instantly diverge from previous training values, which results in degraded system performance. Accordingly, there is a need in the art for methods and apparatus for performing zero-forcing beamforming, which do not involve resource-intensive training procedures. Further, there is a need in the art for methods and apparatus for performing zero-forcing beamforming, which are less likely to suffer from performance degradation resulting from ever-changing channel conditions.